• Smart Structures and Systems
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• 제24권4호
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• pp.427-434
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• 2019

This study investigates the structural integrity of the amphibious tour bus under the rollover condition. The multi-purpose bus called Dual Mode Tour Bus (DMTB) which explores on land and water has been designed on top of a truck platform. Prior to the fabrication of new upper body and sailing equipment of DMTB, computational analysis investigates the rollover protection of the proposed structure including superstructure, wheels, and axles. The Computer-Aided Design (CAD) of the whole vehicle model is meshed and preprocessed under high performance using the Altair HyperMesh to attain the best mesh model suited for finite element analysis (FEA) on the proposed system. Meanwhile, the numerical model is analyzed by employing LS-DYNA to evaluate the superstructure strength. The numerical model includes detail information about the microstructure and considers wheels and axles as rigid bodies but excludes window glasses, seats, and interior parts. Based on the simulation analysis and proper modifications especially on the rear portion of the bus, the local stiffness significantly increased. The vehicle is rotated to the contact point on the ground based on the mathematical method presented in this study to save computational cost. The results show that the proposed method of rollover analysis is highly significant not only in bus rollover tests but in crashworthiness studies for other application. The critical impartments in our suggested dual-purpose bus accepted and passed "Economic Commission for Europe (ECE) R66".

• Computers and Concrete
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• 제20권6호
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.

• Geomechanics and Engineering
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• 제17권1호
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• pp.109-118
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• 2019

The movement of soil along a slope during rainfall can cause serious economic damage and can jeopardize human life. Accordingly, predicting slope stability during rainfall is a major issue in geotechnical engineering. Due to rainwater penetrating the soil, the negative pore water pressure will decrease, in turn causing a loss of shear strength in the soil and ultimately slope failure. More seriously, many constructions such as houses and transmission towers built in/on slopes are at risk when the slopes fail. In this study, the numerical simulation using 2D finite difference program, which can solve a fully coupled hydromechanical problems, was used to evaluate the effects of soil properties, rainfall conditions, and the location of a foundation on the slope instability and slope failure mechanisms during rainfall. A slope with a transmission tower located in Namyangju, South Korea was analyzed in this study. The results showed that the correlation between permeability and rainfall intensity had an important role in changing the pore water pressure via controlling the infiltrated rainwater. The foundation of the transmission tower was stable during rainfall because the slope failure was estimated to occur at the toe of the slope, and did not go through the foundation.

• Nuclear Engineering and Technology
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• 제55권4호
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• pp.1244-1249
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• 2023

Recently, the Kingdom of Saudi Arabia (KSA) announced the development of first-of-a-kind(FOAK) and most advanced futuristic vertical city and named as 'The LINE'. The project will have zero carbon dioxide emissions and will be powered by clean energy sources. Therefore, a study was designed to understand which clean energy sources might be a better choice. Because of its nearly carbon-free footprint, nuclear energy may be a good choice. Nowadays, the development of very small modular reactors (vSMRs) is gaining attention due to many salient features such as cost efficiency and zero carbon emissions. These reactors are one step down to actual small modular reactors (SMRs) in terms of power and size. SMRs typically have a power range of 20 MWe to 300 MWe, while vSMRs have a power range of 1-20 MWe. Therefore, a study was conducted to discuss different vSMRs in terms of design, technology types, safety features, capabilities, potential, and economics. After conducting the comparative test and analysis, the fuel cycle modeling of optimal and suitable reactor was calculated. Furthermore, the levelized unit cost of electricity for each reactor was compared to determine the most suitable vSMR, which is then compared other generation SMRs to evaluate the cost variations per MWe in terms of size and operation. The main objective of the research was to identify the most cost effective and simple vSMR that can be easily installed and deployed.

• Steel and Composite Structures
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• 제46권6호
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Structural Engineering and Mechanics
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• 제87권2호
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• pp.151-164
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• 2023

The latest earthquake's costly repairs and economic disruption were brought on by excessive residual drift. Self-centering systems are one of the most efficient ways in the current generation of seismic resistance system to get rid of and reduce residual drift. The mechanics and behavior of the self-centering system in response to seismic forces were impacted by a number of important factors. The amount of post-tensioning (PT) force, which is often employed for the standing posture after an earthquake, is the first important component. The energy dissipater element is another one that has a significant impact on how the self-centering system behaves. Using the damper as a replaceable and affordable tool and fuse in self-centering frames has been recommended to boost energy absorption and dampening of structural systems during earthquakes. In this research, the self-centering steel moment frame connections are equipped with cushion flexural dampers (CFDs) as an energy dissipator system to increase energy absorption, post-yielding stiffness, and ease replacement after an earthquake. Also, it has been carefully considered how to reduce permanent deformations in the self-centering steel moment frames exposed to seismic loads while maintaining adequate stiffness, strength, and ductility. After confirming the FE model's findings with an earlier experimental PT connection, the behavior of the self-centering connection using CFD has been surveyed in this study. The FE modeling takes into account strands preloading as well as geometric and material nonlinearities. In addition to contact and sliding phenomena, gap opening and closing actions are included in the models. According to the findings, self-centering moment-resisting frames (SF-MRF) combined with CFD enhance post-yielding stiffness and energy absorption with the least amount of permeant deformation in a certain CFD thickness. The obtained findings demonstrate that the effective energy dissipation ratio (β), is increased to 0.25% while also lowering the residual drift to less than 0.5%. Also, this enhancement in the self-centering connection with CFD's seismic performance was attained with a respectable moment capacity to beam plastic moment capacity ratio.

• Steel and Composite Structures
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• 제52권2호
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• pp.135-143
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• 2024

Once the foundation displacement of the transmission tower occurs, additional stress will be generated on the tower members, which will affect the seismic response of transmission tower-line systems (TTLSs). Furthermore, existing research has shown that the reciprocating slippage of joints needs to be considered in the seismic analysis. The hysteretic behavior of joints is obtained by model tests or numerical simulations, which leads to the low modeling efficiency of TTLSs. Therefore, this paper first utilized numerical simulation and model tests to construct a BP neural network for predicting the skeleton curve of joints, and then a numerical model for a TTLS considering the bolt slippage was established. Then, the seismic response of the TTLS under foundation displacement was studied, and the member stress changes and the failed member distribution of the tower were analyzed. The influence of foundation displacement on the seismic performance were discussed. The results showed that the trained BP neural network could accurately predict the hysteresis performance of joints. The slippage could offset part of the additional stress caused by foundation settlement and reduce the stress of some members when the TTLS with foundation settlement was under earthquakes. The failure members were mainly distributed at the diagonal members of the tower leg adjacent to the foundation settlement and that of the tower body. To accurately analyze the seismic performance of TTLSs, the influence of foundation displacement and the joint effect should be considered, and the BP neural network can be used to improve modeling efficiency.

• Geomechanics and Engineering
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• 제8권4호
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• pp.615-630
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• 2015

The use of helical anchors has been extensively beyond their traditional use in the electrical power industry in recent years. They are commonly used in more traditional civil engineering infrastructure applications so that the advantages of rapid installation and immediate loading capability. The majority of the research has been directed toward the tensile uplift behaviour of single anchors (only one plate) by far. However, anchors commonly have more than one plate. Moreover, no thorough numerical and experimental analyses have been performed to determine the ultimate pullout loads of multi-plate anchors. The understanding of behavior of these anchors is unsatisfactory and the existing design methods have shown to be largely inappropriate and inadequate for a framework adopted by engineers. So, a better understanding of helical anchor behavior will lead to increased confidence in design, a wider acceptance as a foundation alternative, and more economic and safer designs. The main aim of this research is to use numerical modeling techniques to better understand multi-plate helical anchor foundation behavior in soft clay soils. Experimental and numerical investigations into the uplift capacity of helical anchor in soft clay have been conducted in this study. A total of 6 laboratory tests were carried out using helical anchor plate with a diameter of 0.05 m. The results of physical and computational studies investigating the uplift response of helical anchors in soft clay show that maximum resistances depend on anchor embedment ratio and anchor spacing ratio S/D. Agreement between uplift capacities from laboratory tests and finite element modelling using PLAXIS is excellent for anchors up to embedment ratios of 6.

• Structural Monitoring and Maintenance
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• 제4권1호
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• pp.17-31
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• 2017

Since extensometers are used to determine the absolute deformation of foundation and abutments and all results are obtained in reference to the base rod, the accuracy of these results has been constantly a subject of debate. In this regard, locating and installing extensometers outside the range of effect zone is also another challenge. The main purpose of this paper is to investigate and modify extensometers results based on the mentioned issues. For this aim, the concrete RCC Zhaveh dam in Iran was selected as the case study. To study the results of extensometers installed in this dam, first, the 3DEC_DP 5.00 software was applied for numerical modeling. Parameters such as discontinuities, dead load and piezometric pressure in the interface of concrete and rock were considered. Next, using the results obtained from 6 extensometers in foundation and abutments and 4 clinometers in dam body, the numerical model was calibrated through back analysis method. The results indicate that the base rod is moved and is not recommended being used as the base point. In other words, because installation of base anchor outside the range of effect zone is not possible due to the operational and economic considerations, the obtained results are not accurate enough. The results indicate a considerable 2-3 mm displacement of the base rod (location of the base anchor) in reference to the real zero point location, which must be added to the base rod results.

• Steel and Composite Structures
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• 제34권5호
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• pp.681-698
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• 2020

The paper addresses contribution to the modeling and optimization of major machinability parameters (cutting force, surface roughness, and tool wear) in finish dry hard turning (FDHT) for machinability evaluation of hardened AISI grade die steel D₃ with PVD-TiN coated (Al₂O₃-TiCN) mixed ceramic tool insert. The turning trials are performed based on Taguchi's L₁₈ orthogonal array design of experiments for the development of regression model as well as adequate model prediction by considering tool approach angle, nose radius, cutting speed, feed rate, and depth of cut as major machining parameters. The models or correlations are developed by employing multiple regression analysis (MRA). In addition, statistical technique (response surface methodology) followed by computational approaches (genetic algorithm and particle swarm optimization) have been employed for multiple response optimization. Thereafter, the effectiveness of proposed three (RSM, GA, PSO) optimization techniques are evaluated by confirmation test and subsequently the best optimization results have been used for estimation of energy consumption which includes savings of carbon footprint towards green machining and for tool life estimation followed by cost analysis to justify the economic feasibility of PVD-TiN coated Al₂O₃+TiCN mixed ceramic tool in FDHT operation. Finally, estimation of energy savings, economic analysis, and sustainability assessment are performed by employing carbon footprint analysis, Gilbert approach, and Pugh matrix, respectively. Novelty aspects, the present work: (i) contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-60 HRC, (ii) demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing ceramic tool in hard turning processes considering technological, economical and ecological aspects, which are helpful and efficient from industrial point of view, (iii) provides environment friendliness, cleaner production for machining of hardened steels, (iv) helps to improve the desirable machinability characteristics, and (v) serves as a knowledge for the development of a common language for sustainable manufacturing in both research field and industrial practice.